# Publications

## 2021

### Generalizable dimensions of human cortical auditory processing of speech in natural soundscapes

Boos, M., Lücke, J., & Rieger, J. W. (2021). Generalizable dimensions of human cortical auditory processing of speech in natural soundscapes: A data-driven ultra high field fMRI approach. *NeuroImage*, 237, 118106.

Speech comprehension in natural soundscapes rests on the ability of the auditory system to extract speech information from a complex acoustic signal with overlapping contributions from many sound sources. Here we reveal the canonical processing of speech in natural soundscapes on multiple scales by using data-driven modeling approaches to characterize sounds to analyze ultra high field fMRI recorded while participants listened to the audio soundtrack of a movie.

[Paper link](https://www.sciencedirect.com/science/article/pii/S1053811921003839)

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## 2020

### The role of auxiliary parameters in evaluating voxel-wise encoding models for 3T and 7T BOLD fMRI data

Boos, M., Guntupalli, J. S., Rieger, J. W., & Hanke, M. (2020). The role of auxilliary parameters in evaluating voxel-wise encoding models for 3T and 7T BOLD fMRI data. *bioRxiv*

In neuroimaging, voxel-wise encoding models are a popular tool to predict brain activity elicited by a stimulus. To evaluate the accuracy of these predictions across multiple voxels, one can choose between multiple quality metrics. However, each quality metric requires specifying auxiliary parameters such as the number and selection criteria of voxels, whose influence on model validation is unknown.

[Paper link](https://www.biorxiv.org/content/10.1101/2020.04.07.029397v2) | [Code](https://github.com/psychoinformatics-de/studyforrest-paper-pandoraencoding)

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## 2016

### Probabilistic inference: task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modeling

Boos, M., Seer, C., Lange, F., & Kopp, B. (2016). Probabilistic inference: task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modeling. *Frontiers in psychology*, 7, 755.

We use Hierarchical Bayesian Modelling to compare models of decision making under uncertainty with varying degrees of inter-individual differences.

[Paper link](https://www.frontiersin.org/articles/10.3389/fpsyg.2016.00755/full)